The Matthew effect in science funding

Abstract

A classic thesis is that scientific achievement exhibits a “Matthew effect”: Scientists who have previously been successful are more likely to succeed again, producing increasing distinction. We investigate to what extent the Matthew effect drives the allocation of research funds. To this end, we assembled a dataset containing all review scores and funding decisions of grant proposals submitted by recent PhDs in a €2 billion granting program. Analyses of review scores reveal that early funding success introduces a growing rift, with winners just above the funding threshold accumulating more than twice as much research funding (€180,000) during the following eight years as nonwinners just below it. We find no evidence that winners’ improved funding chances in subsequent competitions are due to achievements enabled by the preceding grant, which suggests that early funding itself is an asset for acquiring later funding. Surprisingly, however, the emergent funding gap is partly created by applicants, who, after failing to win one grant, apply for another grant less often.

External Tests of Peer Review Validity Via Impact Measures

“…While it may be able to separate good and flawed proposals, discrimination amongst the top tier proposals or applicants may be more difficult, which is what the system is currently charged to do given recent funding levels (Fang et al, 2016). Nevertheless, this seems to depend on the metric used, as some studies found a high degree of discrimination when tracking career success of funded and top tier unfunded applicants (Fang and Meyer, 2003;Hornbostel et al, 2009;Escobar-Alvarez and Myers, 2013), although the effects of funding itself have to be teased out (Bol et al, 2018). Also, some level of validity was found with studies involving patents, post-funding review of outcomes and levels of collaboration as well, suggesting validity across multiple outputs.…”

“…Mavis and Katz (2003) also observed higher post-award funding rates for successful applicants compared to unsuccessful ones, although there was no control for review score. Similarly, others have shown that, despite similar qualifications, funded applicants are more successful in gaining future funding and securing tenure track positions compared to unfunded applicants (Bol et al, 2018;Heggeness et al, 2018). However, many of these observations may be the result of the funding itself enabling future funding, as well as lowered levels of resubmissions by unfunded applicants.…”

Research priorities for liver glycogen storage disease: An international priority setting partnership with the James Lind Alliance

“…Currently, global funding in the biomedical research field involves billions of dollars and millions of people . In this landscape, eminent scientists who obtain funding determine the course of research and this has not changed in the past decades . Ideally decisions about research funding should take patients, carers, and healthcare professionals into consideration, but often they are not involved in the choice, design, performance, analysis, and dissemination of research.…”

Make‐or‐Break: Chasing Risky Goals or Settling for Safe Rewards?

“…How do people form beliefs about their own skill level and the relationship between effort, luck, and achievement in a task? They may use social comparisons or individual experiences in similar tasks to inform their beliefs about their skill levels (Bandura, ; Bol et al., ; Frank, ). Repeated interactions with a task or continuous feedback about performance may help them to hone their intuitions about how effort and luck jointly define the chances of achieving a goal (Weiner, ; Weiner & Kukla, ).…”

“…Despite its simplicity, the setting Kukla described is of broad interest, because it exposes the challenging nature of a common class of effort allocation problems encountered in daily life. Similar discontinuities between the parameters that characterize the decision‐making problem (i.e., rewards, abilities, constraints) and the normatively prescribed or observed behavior (i.e., time allocation) have been uncovered in various domains, such as allocating time among learning tasks (Son & Sethi, ), effort in writing prestigious grants (Bol, de Vaan, & van de Rijt, ), and allocating attention in perceptual decisions (Morvan & Maloney, ), potentially hinting at a common underlying problem structure.…”